class_docstr: A linear value function (baseline) based on features.
  
  Args:
      env_spec (garage.envs.env_spec.EnvSpec): Environment specification.
      reg_coeff (float): Regularization coefficient.
      name (str): Name of baseline.
docstr: Predict value based on paths.
  
  Args:
      paths (list[dict]): Sample paths.
  
  Returns:
      numpy.ndarray: Predicted value.
docstr_tok: ["Predict","value","based","on","paths",".","Args",":","paths","(","list","[","dict","]",")",":","Sample","paths",".","Returns",":","numpy.ndarray",":","Predicted","value","."]
code: def predict(self, paths):
      """Predict value based on paths.
  
      Args:
          paths (list[dict]): Sample paths.
  
      Returns:
          numpy.ndarray: Predicted value.
  
      """
      if self._coeffs is None:
          return np.zeros(len(paths["observations"]))
      return self._features(paths).dot(self._coeffs)
  
file_path: garage/src/garage/np/baselines/linear_feature_baseline.py
imports: import numpy
  from garage.np.baselines.baseline import Baseline
label_desc: Use image node_id 6 for calling the LinearFeatureBaseline obj's underlying member method code with example usage: obj.predict(paths) and returns: self, np